Merge remote-tracking branch 'upstream/master' into ENH/unsafe_div

6 files changed, 60 insertions, 3 deletions

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index 588a45ea43..f56521dac0 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -379,9 +379,11 @@ tf_cc_test(
     srcs = ["gradients/math_grad_test.cc"],
     deps = [
         ":cc_ops",
+        ":client_session",
         ":grad_op_registry",
         ":grad_testutil",
         ":gradient_checker",
+        ":gradients",
         ":math_grad",
         ":testutil",
         "//tensorflow/core:lib_internal",
@@ -626,7 +628,6 @@ tf_cc_binary(
     copts = tf_copts(),
     linkopts = select({
         "//tensorflow:windows": [],
-        "//tensorflow:windows_msvc": [],
         "//tensorflow:darwin": [
             "-lm",
             "-lpthread",
diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc
index b353accddc..e9173227aa 100644
--- a/tensorflow/cc/gradients/array_grad.cc
+++ b/tensorflow/cc/gradients/array_grad.cc
@@ -120,6 +120,24 @@ Status SplitGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Split", SplitGrad);
 
+Status FillGrad(const Scope& scope, const Operation& op,
+                const std::vector<Output>& grad_inputs,
+                std::vector<Output>* grad_outputs) {
+  // y = fill(fill_shape, x)
+  // No gradient returned for the fill_shape argument.
+  grad_outputs->push_back(NoGradient());
+  // The gradient for x (which must be a scalar) is just the sum of
+  // all the gradients from the shape it fills.
+  // We use ReduceSum to implement this, which needs an argument providing
+  // the indices of all the dimensions of the incoming gradient.
+  // grad(x) = reduce_sum(grad(y), [0..rank(grad(y))])
+  auto all_dims = Range(scope, Const(scope, 0), Rank(scope, grad_inputs[0]),
+                        Const(scope, 1));
+  grad_outputs->push_back(ReduceSum(scope, grad_inputs[0], all_dims));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Fill", FillGrad);
+
 Status DiagGrad(const Scope& scope, const Operation& op,
                 const std::vector<Output>& grad_inputs,
                 std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/array_grad_test.cc b/tensorflow/cc/gradients/array_grad_test.cc
index d09275b648..f41de3dc20 100644
--- a/tensorflow/cc/gradients/array_grad_test.cc
+++ b/tensorflow/cc/gradients/array_grad_test.cc
@@ -108,6 +108,14 @@ TEST_F(ArrayGradTest, SplitGrad) {
   RunTest({x}, {x_shape}, y.output, {y_shape, y_shape});
 }
 
+TEST_F(ArrayGradTest, FillGrad) {
+  TensorShape x_shape({});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
+  TensorShape y_shape({2, 5, 3});
+  auto y = Fill(scope_, {2, 5, 3}, x);
+  RunTest(x, x_shape, y, y_shape);
+}
+
 TEST_F(ArrayGradTest, DiagGrad) {
   TensorShape x_shape({5, 2});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index cd215f740d..1329b568ab 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -1022,6 +1022,26 @@ Status ProdGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Prod", ProdGrad);
 
+Status SegmentSumGrad(const Scope& scope, const Operation& op,
+                      const std::vector<Output>& grad_inputs,
+                      std::vector<Output>* grad_outputs) {
+  // The SegmentSum operation sums segments of the Tensor that have the same
+  // index in the segment_ids parameter.
+  // i.e z = [2, 3, 4, 5], segment_ids [0, 0, 0, 1]
+  // will produce [2 + 3 + 4, 5] = [9, 5]
+  // The gradient that will flow back to the gather operation will look like
+  // [x1, x2], it will have the same shape as the output of the SegmentSum
+  // operation. The differentiation step of the SegmentSum operation just
+  // broadcast the gradient in order to retrieve the z's shape.
+  // dy/dz = [x1, x1, x1, x2]
+  grad_outputs->push_back(Gather(scope, grad_inputs[0], op.input(1)));
+
+  // stop propagation along segment_ids
+  grad_outputs->push_back(NoGradient());
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("SegmentSum", SegmentSumGrad);
+
 // MatMulGrad helper function used to compute two MatMul operations
 // based on input matrix transposition combinations.
 Status MatMulGradHelper(const Scope& scope, const bool is_batch,
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index 147428cc39..c16938322c 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -45,10 +45,10 @@ using ops::Placeholder;
 using ops::Pow;
 using ops::Prod;
 using ops::RealDiv;
+using ops::SegmentSum;
 using ops::SquaredDifference;
 using ops::Sub;
 using ops::Sum;
-using ops::Where3;
 
 // TODO(andydavis) Test gradient function against numeric gradients output.
 // TODO(andydavis) As more gradients are added move common test functions
@@ -932,5 +932,14 @@ TEST_F(NaryGradTest, Prod) {
   RunTest({x}, {x_shape}, {y}, {y_shape});
 }
 
+TEST_F(NaryGradTest, SegmentSum) {
+  TensorShape x_shape({3, 4});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
+  auto y = SegmentSum(scope_, x, {0, 0, 1});
+  // the sum is always on the first dimension
+  TensorShape y_shape({2, 4});
+  RunTest({x}, {x_shape}, {y}, {y_shape});
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc
index 98be66a6ad..3830416159 100644
--- a/tensorflow/cc/saved_model/loader.cc
+++ b/tensorflow/cc/saved_model/loader.cc
@@ -170,7 +170,8 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir,
       variables_directory, MetaFilename(kSavedModelVariablesFilename));
   if (!Env::Default()->FileExists(variables_index_path).ok()) {
     LOG(INFO) << "The specified SavedModel has no variables; no checkpoints "
-                 "were restored.";
+                 "were restored. File does not exist: "
+              << variables_index_path;
     return Status::OK();
   }
   const string variables_path =